---
name: security-and-privacy
description: "Security and Privacy: 15 concepts, no maths assumed."
version: 1.0.0
author: Learn AI
license: CC-BY-4.0
metadata:
  hermes:
    tags: [Security, And, Privacy]
    related_skills: [data-engineering-and-analytics, deep-learning, llms-and-generative-ai, machine-learning, mathematical-foundations, software-engineering]
    source: https://basavapurushottam.com/learn-ai
---
# Security and Privacy

15 concepts, written for an adult with no computer science and no mathematics past school arithmetic. Each concept is a file under `references/`; load one when it is the thing being asked about rather than reading the whole domain.


## When to Use

- The reader asks about security and privacy and wants it explained from the ground up rather than summarised.

- A term below comes up and you want the plain-language account of it, with a worked example.

- You need to know what a concept rests on before teaching it — every reference lists its prerequisites.


## When NOT to Use

- The reader wants current events, papers or library documentation. This is a curriculum, not a news source, and it does not update itself.

- The reader wants code they can ship. Examples here are written to be understood, not deployed.


## How to Run

Open the file listed beside a concept in Quick Reference below. The files live in the `references` directory, are plain Markdown with no front matter and no dependencies — read the one you want and ignore the rest.


## Quick Reference


### AI Security

- `references/adversarial-examples.md` — **Adversarial Examples**: Inputs perturbed almost imperceptibly to a human that cause confident misclassification.
- `references/adversarial-patches.md` — **Adversarial Patches**: Physical or localised patterns that reliably fool a vision system regardless of where t…
- `references/carlini-wagner-attack.md` — **Carlini-Wagner Attack**: A strong optimisation-based attack that finds minimal perturbations causing misclassifi…
- `references/data-poisoning.md` — **Data Poisoning**: Corrupting training data to implant a backdoor or degrade a model, attacking the pipeli…
- `references/gradient-obfuscation.md` — **Gradient Obfuscation**: Defences that hide or break gradients, appearing to stop attacks while leaving the mode…
- `references/intrusion-detection.md` — **Intrusion Detection**: Identifying malicious activity in networks or hosts through signatures, anomalies or le…
- `references/liveness-detection.md` — **Liveness Detection**: Verifying that biometric input comes from a live person rather than a photo, replay or…
- `references/mitre-atlas.md` — **MITRE ATLAS**: A knowledge base of adversary tactics and techniques specific to AI systems, structured…
- `references/membership-inference.md` — **Membership Inference**: Determining whether a specific record was in a model's training set, from the model's b…
- `references/model-extraction.md` — **Model Extraction**: Reconstructing a proprietary model's behaviour by querying it systematically and traini…
- `references/owasp-llm-top-10.md` — **OWASP LLM Top 10**: A consensus list of the most critical security risks in LLM applications, from prompt i…
- `references/prompt-injection.md` — **Prompt Injection**: Untrusted content instructing a model to ignore its original directions, treating data…
- `references/red-and-purple-teaming.md` — **Red and Purple Teaming**: Adversarial testing by a red team, and collaborative testing where red and blue teams w…

### Cryptography

- `references/post-quantum-cryptography.md` — **Post-Quantum Cryptography**: Cryptographic algorithms designed to resist attack by future quantum computers, now bei…

### Privacy Engineering

- `references/federated-learning.md` — **Federated Learning**: Training a shared model across many devices or institutions that exchange updates rathe…


## Pitfalls

- The concepts are ordered by what rests on what, not by difficulty. A later concept is not always harder; it is later because something else has to come first.

- Explanations avoid notation the reader has not met. If you reintroduce standard symbols, say what they mean, or you undo the thing this curriculum is for.


## Verification

`ls references/ | wc -l` should print 15.


---

From [Learn AI](https://basavapurushottam.com/learn-ai). Prose is licensed CC BY 4.0.
